Essential demand drivers to consider in supply chain forecasting

By Erik de Vos

In one of our previous blogs, we mentioned various types of data integral to supply chain forecasting: sales data, factual data, plan data, and market indicators. To shed more light on the smart-touch planning and forecasting approach, let’s dive deeper into some examples, introducing the concept of demand drivers. These are elements that drive change or explain shifts in your demand. Since they help to increase forecast accuracy and reduce bias, both play essential roles in supply chain forecasting.


data types in supply chain forecasting

1. Promotions 

Take promotions, for instance – they’re catalysts for changes in demand. If you organize promotions, it’s reasonable to expect a shift in demand for your products.  

Now, imagine knowing when you conducted past promotions and when you plan future ones. Armed with marker information during promotions, a machine could forecast potential demand changes and incorporate them into your forecast. By enriching promotion details – promo mechanisms, discount percentages, marketing investments, or indicators like second placements and folder advertisements – a machine can learn to link this information to sales data effects (peaks and dips) for a more accurate forecast. 

Consider the alternative – planning promotions without machine learning. It often involves copy-pasting from previous efforts, assuming a slight sales increase even with the same promotions, expecting a uniform uplift on all products, and assuming consistent phasing of promotional effects for all customers. Due to data complexity and the lower priority given to supply chain forecasting promotions compared to other tasks, these shortcuts become the only way to deliver a forecast.


2. Order book data

Now, what about elements providing insights into likely changes in demand? Enter the order book, a typical example readily available in most ERP systems. Depending on your business, your order book horizon may differ. What if we could leverage this data to automate processes like allocation and short-term forecast tuning? By collecting data on when orders are received and their requested delivery dates, you can predict your order book’s future shape. Offset against your latest forecast, organized with supply and inventory considerations, you can identify service risks and overstock situations earlier, even asking the machine to distribute the remaining inventory based on likely demand.


3. Market data

Moving to another example – the increasing focus on using market data. Your demand isn’t solely influenced by your actions but also by the broader market context. Incorporating information on how your market evolves and will evolve, can aid in driving part of your supply chain forecasting effort.  

Market growth indicators can help to tune the overall trend you take into your forecast. Information on past and future external events not under your control can be used to predict event-based disruptive demand e.g. during a World Cup. Information on how the renovation and housing market will evolve can help you to predict the longer-term demand for construction materials.


marketing indicators in supply chain forecasting

Granted, these examples focus more on trends and mid-term effects. Yet, in the dynamic landscape of continuous information and opinions, you’ll also encounter market effects impacting demand on much shorter notice. For instance, weather outlook information can predict short-term swings in demand, enabling you to steer logistical execution capacity effectively. Or think about social media product trends that can suddenly boost or destroy sales.  

In relation to market data, we would expect to recognize 2 elements in a well-structured smart-touch planning and forecasting setup: 

  • Firstly, fast insights and a decision-making framework that uses available information to detect the disruption in the market quickly and manage it.  
  • Secondly, the ability to translate market insights & disruptions into assumptions for your mid-term forecast, allowing for proper steering of the supply chain.

ffective supply chain forecasting with demand drivers

So, bringing smart-touch to life essentially involves understanding your demand drivers – those influencing demand and those providing valuable information. Once you grasp your drivers, maintain clarity on what they contribute to your forecast. Using the order book finetunes your shorter-term horizon but doesn’t impact your longer-term forecast. Promotions bring one-off uplift effects but also introduce dips. Sales history captures regular and seasonal sales effects. Market indicators tune trends or prepare for disruptions. Expecting the right consequence of a demand driver ensures you set the right things in motion. And that is the essence of smart-touch planning and forecasting.


external drivers in supply chain forecasting

Ready to elevate supply chain forecasting and unlock the full potential of demand planning?

Despite some huge steps we have taken in demand planning, many planning teams remain stuck in the labor-intensive full-touch forecasting phase. It’s time demand planning teams unlock the true value of demand planning through smart-touch forecasting. 

That’s why we’ve created the smart-touch forecasting roadmap. 

In this roadmap, we’ll dig deeper into the hype surrounding complete forecast automation, advocating the crucial role of human expertise when applying statistical and machine-learning models. And we’ll provide you with practical steps to accelerate progress in your supply chain forecasting maturity level. Get your copy here.


future proof demand forecasting technique: smart touch forecasting

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